income inequality in post-communist central and eastern european countries

7/24/2019 Income Inequality in Post-communist Central and Eastern European Countries

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EASTERN JOURNAL OF EUROPEAN STUDIES Volume 5, Issue 1, June 2014 5

Income inequality in post-communist Central and

Eastern European countries

Sara ROSE*, Crina VIJU**

Abstract

Income inequality has become an important issue in Central and EasternEuropean countries during their transition process. This study constructs amodel incorporating different categories of factors that impact inequality and

tests whether the wealth of a country makes a difference in these relationships.This article shows that different income categories of Central and Eastern

European transition countries do experience different relationships betweenincome inequality and its contributing factors: economic, demographic,

political, and cultural and environmental. The resulting Random Effects models

of the best fit incorporate economic and political factors and show differences inmagnitude, direction, and in their significance. These findings add to the

literature by taking a cross-country and cross-income view on the impact of

various factors.

Keywords:Central and Eastern European countries, income inequality, transitioneconomies

1. Introduction

Equality was one of the lofty goals of Communism in Central andEastern Europe. It is a commendable aim but proved unrealistic, even though

levels of income inequality in those countries were much lower during theCommunist era than they are at present (Milanovic, 1998). Decreasing levels ofinequality is an important issue but how should it be addressed in the context ofdifferent countries? Do differences in countries characteristics materiallyimpact inequality? This paper will seek to show that, in terms of transitioneconomies, countries with different levels of wealth experience differentrelationships between income inequality and its contributing factors. The goal of

*Sara Rose is PhD candidate at Wilfrid Laurier University, Waterloo, Canada; e-mail:[email protected].**Crina Viju is assistant professor at Carleton University, Ottawa, Canada; e-mail:[email protected].


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6 Sara ROSE, Crina VIJU

this study is to firstly construct a model incorporating different categories offactors that impact inequality within a country and, secondly, to test whether or

not the income category makes a difference in income inequality, where theincome category is the level of a countrys income, as determined by the WorldBanks Atlas Method using Gross National Income (GNI) per capita, divided

into low-, middle-, and high-income categories. It will illustrate that there aredifferent relationships between income inequality and its contributing factorsdependent on the particular income category.

The paper is organized as follows. Sections 2 and 3 review contributionsto income inequality in developing and transition economics literature, followed

by a description of the data used in the analysis. Section 5 will present anddescribe the empirical methodology and results. Section 6 concludes.

2. Income inequality and economic development

The root of much of the study on income inequality is found in Kuznets

(1955) paper, Economic Growth and Income Inequality. It offers an attempt at

explaining income inequality by studying the effects of savings and the shift infocus from the agricultural to the industrial sector. It also compares incomeinequality in developed versus developing countries, providing a logical,speculative argument that income levels would be more unequal inunderdeveloped countries due to the disproportionate income share of the

highest income bracket. As this paper seeks to show and as found in variousother studies, Kuznets recognizes that there are differences in income inequalityacross different stages of economic growth (Kuznets, 1955).

Subsequent research has built upon Kuznets work, looking at changesin income inequality in terms of a countrys economic growth and variousfactors, economic, political or cultural, that have an impact on inequality. Somestudies have found that there is not a strong link between inequality and growth,instead finding strong relationships between growth and poverty (Deininger andSquire, 1996). Studies focusing on impacting factors have found mixed results

based on the country or group of countries selected. For example, whenresearching a potential relationship within OECD countries between inequalityand imports of manufactured goods, a relationship was found when non-European countries were included, but the relationship did not exist if thesecountries were excluded from the sample (Gustafsson and Johansson, 1999).

The study of income inequality is of particular importance within thecontext of developing countries due to its effect on the economy. Theeconomically damaging effects are more severe for weak economies and canresult in significantly high poverty rates. Income inequality can cause economicinefficiencies within a country: less people can qualify for credit, due to their

lack of collateral; savings would be lower, as the middle class, which is a smallgroup in these countries, has the highest average savings rate; and there would


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INCOME INEQUALITY IN POST-COMMUNIST CEE COUNTRIES 7

be an inefficient allocation of assets as, for example, the rich own the majority ofland (Todaro and Smith, 2009). Income inequality is also detrimental from social

and political perspectives as it can increase socio-political instability (Park,1996) and facilitate rent-seeking behaviour including cronyism (Samanta andHeyse, 2006), whereas others argue that high enough inequality can reducegrowth (Ravallion, 1997). Ravallion, in the same 1997 study, also showed thatcountries with high levels of income inequality gain less from economic growthin terms of poverty alleviation as the impact on absolute poverty is reduced. Theinverse relation, the impact of growth on income inequality, is also unclear.Empirical evidence has shown that changes in income distribution are generallynot correlated with growth; inequality increases in roughly half of the cases andin the other half, it decreases (Bruno, Ravallion, and Squire, 1998).

Not only is it challenging to form solid conclusions about a relationshipbetween income inequality and growth, it is also difficult to form generalconclusions about income inequality in developing countries. Differences cancome from different regions and time periods (Wood, 1997), the openness of thecountry to globalization (Rudra, 2004), and numerous other factors. This

perspective is extended to this study, where differences in income categoriesacross transition economies will be analyzed. Even within a country there can bedifferences, such as the impact of economic growth among various groups of the

poor (Ravallion, 2001). These differences make broad cross-country analysis

more difficult when searching for general conclusions; therefore, conclusionsmust often be more specific to particular situations.

3. Inequality and transition economies

The socialist system saw lower levels of income inequality incomparison to countries at similar levels of development but all transitioncountries registered an increase in inequality after the fall of Communism(Bandelj and Muhutga, 2010). The average Gini coefficient of disposableincome in transition economies increased from 24 to 33 in just nine years and an

increase in the dispersion of Gini coefficients could also be observed(Milanovic, 1998). At first, this increase in income inequality was tolerated bythe people as it was seen as a sign of increased potential opportunities. Tolerancethen decreased over time as the income distribution process was increasinglyseen as unfair and people became dissatisfied with the economic situation withintheir countries (Grosfeld and Senik, 2010), and with good reason; economicreforms in transition economies have stagnated and convergence with the livingstandards of advanced economies has stalled (EBRD, 2013).

Many studies seem to agree that the different levels of income inequalityseen in transition economies, the phenomenon described by Milanovic (1998), are

due to different governmental approaches taken towards stabilization, liberalization,privatization, and the resulting policies (Ivanova, 2007; Bandelj and Mahutga, 2010;


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Porras, 2010). Certain policies had a negative impact on equality, including thosewhich decreased social services (Ivanova, 2007) and fostered foreign investment

over domestic investment (Bandelj and Mahutga, 2010). A lack of certain policiesalso negatively impacted equality, such as the lack of an educational policy toencourage adaptability to changing technology (Aghion and Commander, 1999).Beyond policy, some governments were more predisposed to combat risinginequality through higher rates of government effectiveness and more financialresources (Grimalda, Barlow, and Meschi, 2010). Further differences can beexplained through the different styles of reform, such as their speed and sequence(Aristei and Perugini, 2012; Ivanova, 2007), initial conditions prior to the transition(Porras, 2010), and different models of capitalism implemented in transitioneconomies (Izyumov and Calxon, 2009).

It is the importance of the issue of income inequality, the increasingdissatisfaction with income inequality, and the array of contributing factors thathave led to this study. It is the scope of this study to analyze the different impactsof certain factors on income inequality within low-, middle-, and high-incomeCentral and Eastern European transition economies. This study takes into accountfour main categories of contributing factors of income inequality: economic,demographic, political, and cultural and environmental. These four categories havebeen directly based on Kaasas (2005) extensive workon the subject.

The economic indicators have been divided into two sub-groups; wealth

and macroeconomic factors. The wealth indicator in this study is GrossDomestic Product (GDP) per capita. Studies that focus specifically on GDP as ameasure of economic wealth have provided ambiguous results. Ogwang (1994)

provides a compelling empirical argument for a relationship between incomeinequality and GDP per capita, finding that both the conditional means andconditional variances of inequality measures follow an inverted U-shape,

bolstering the work of Kuznets. Alternatively, Chowdhury (2003) suggestsstatistical independence between the GDP per capita growth rate and incomeinequality growth rate. The main conclusion drawn from past work is that moreempirical study is necessary (Aghion, Caroli, and Garcia-Penalosa, 1999).

Macroeconomic factors allow for the study of a country within aninternational context. Globalization and increasing financial integration makethis an increasingly important category. The first indicator is Foreign DirectInvestment (FDI). Much of the research done on the effect of FDI on incomeinequality has determined that increased FDI often increases income inequality(Kaasa, 2005; Bandelj and Mahutga, 2010; Grimalda et al., 2010), withAlderson and Nielsen (1999) concluding that higher levels of income inequalityare related to relative dependence on foreign investment. The other indicator isinflation. It is unclear if inflation actually has an impact on income inequality, as

some studies have shown a positive relationship (Parker, 1998; Bulir, 2001),


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while others show no statistically significant relationship (Gustafsson andJohansson, 1999).

Demographic factors look at the characteristics of a countryspopulation. This study uses rural population, representing urbanization, andprimary school completion rate, representing education. The effect ofurbanization on income inequality is not entirely clear. Some studies yield nostatistically significant relationship between income inequality and urbanization(Nomiya, 1992; Li, Squire, and Zou, 1998), while others have found asignificant, positive relationship between these two variables (Ullman, 1996;Nielsen and Alderson, 1997). Studies on education, the powerful social

equalizer (Chu, 2000, p. 39), show that the expansion of education is oftenfound to decrease income inequality (Psacharopoulos and Steier, 1988; Chu,

2000). Further, the primary school completion rate itself is specifically shown todecrease income inequality (Dao, 2008). Interestingly, Sylwester (2002a) foundthat the negative effect of public education expenditures on income inequality islarger in higher income countries.

Political factors consider the governments role in income inequality.

The first indicator is privatization, which is specifically a political factor fortransition economies as it relates to the share of the government sector within acountry. Empirical studies typically yield a significant positive association

between inequality and privatization (Bandelj and Mahutga, 2010; Grimalda et

al., 2010). The second political indicator in this study is the level of politicalrights. In terms of the impact of democratization on income inequality, somestudies show a negative relationship (Beitz, 1982; Durham, 1999; Sylwester,2002b) and others (Nielson and Alderson, 1997) show an insignificantrelationship between the two. Within the context of former socialist countries,studies have shown that a Marxist-Leninist indicator has a negative effect oninequality (Nielson and Alderson, 1997), while democracy has a positive effecton inequality (Gradstein and Milanovic, 2004).

Finally, cultural and environmental factors cover characteristics inherentto the country itself. The indicator for this category is corruption, which is

shown to be connected to cultural traditions (Kaasa, 2005) as, over time it couldbecome entrenched within a culture to the point where it may be culturallyaccepted. Its prevalence in transition economies reached endemic levels

(Kaufmann and Siegelbaum, 1997, p. 419) during the 1990s. The results fromthe relevant research indicate that there is a significant positive correlation

between corruption and income inequality (Li et al., 2000; Gupta, Davoodi, andAlonso-Terme, 2002) when using a variety of indicators for corruption.


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4. Data

This study considers thirteen Central and Eastern European transition

countries: Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia,Lithuania, Moldova, Poland, Romania, Russia, Slovakia, and Ukraine. Thecountries are divided into three categories to analyze whether differences inincome categories cause differences in the relationship between incomeinequality and the factors that contribute to income inequality. The categoriesare low-income (LI), middle-income (MI), and high-income (HI). Thesecategories are consistent with the World Banks categories of lower-middle-income, upper-middle-income, and high income, respectively, and are based onGNI per capita (World Bank, 2011e). The division of the thirteen countries is

outlined in Table 1, listed alphabetically.

Table 1. Division of countries into groups, based on GNI per capita, 2010

Low Income (LI)$1,006-$3,975 GNI per

capita

Middle Income (MI)$3,976-$12, 275 GNI per

capita

High Income (HI)$12,275 GNI per capita or

more

Moldova Belarus Czech RepublicUkraine Bulgaria Estonia

Latvia HungaryLithuania Poland

Romania SlovakiaRussia

Source: World Bank (2011a)

Although the countries are not divided equally between the threecategories, using the World Banks categorization system will allow the resultsof this study to be placed within a global framework of levels of development.All data is yearly and spans from 1990 to 2006, approximating the first fifteenyears of transition. Data prior to 1990 are from the socialist era and thus theymay not necessarily be representative of the true economic situation.

The Gini coefficient is used to represent income inequality. A greatervalue of Gini signifies a higher level of inequality. It is advantageous to use theGini coefficient as it is frequently used and would allow for this study to becompared with other studies and fit into the larger body of work on the subject.One potential limitation of the Gini coefficients from the World IncomeInequality Database is that they are compiled from different sources. The GDP

per capita data has been retrieved from the World Bank and uses the midyearpopulation. It is measured in terms of US dollars, as of 2011.

The first demographic factor is rural population as a percentage of the

total population, where rural area is defined by the national statistical offices ofeach country. This data has been taken from the World Bank and is calculated as


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the difference between total population and urban population (World Bank,

2011h). The second demographic factor is the primary completion rate as a

percentage of the relevant age group from the World Banks dataset. It iscalculated by dividing the number of students completing the last grade ofprimary school, minus repeaters, by the total number of children in the countryof official graduation age (World Bank, 2011g).

The private sector share of GDP data represents privatization, one of thepolitical variables in the model. This data has been obtained through the TransitionReports published by the European Bank for Reconstruction and Development(EBRD, 2011). It is important to note that it includes income generated from bothformal and informal activities. The Political Rights index created by FreedomHouse, which runs on a scale from 1 to 7, with 1 representing most free, is the

other political factor in the model. The index is based on survey data that evaluatesthree categories: electoral process, political pluralism and participation, and

functioning of government (Freedom House, 2011, p. 30).The Control of Corruption index, representing cultural and

environmental factors, was created by the World Bank as part of theirWorldwide Governance Indicators project. It is an indicator on a scaleof -2.5 to2.5 and it represents perceptions of the extent to which public power is

exercised for private gain, including both petty and grand forms of corruption, aswell as capture of the state by elites and private interests (World Bank,

2011j). The FDI net inflow, as obtained from World Bank data, is measured inUS dollars as of 2011 (World Bank, 2011b). Finally, the inflation is obtainedfrom the World Bank and it is measured as the growth rate of the GDP deflator,showing the rate of price change in the economy as a whole (World Bank,

2011f).The main potential issue posed by this data set is that, for several of the

variables, there are missing data points.

5. Empirical Methodology and Results

5.1. Models

All possible combinations involving one variable per category were runindividually, using panel data estimation techniques, to determine the optimalmodel: the general model that best fits the data. The resulting models thatexhibited overall statistical significance at the 5 percent level ( = 0.05) werethen run again, using three different estimation techniques, the pooled OLSmodel, the Fixed Effects model (FEM), and the Random Effects model (REM),dropping insignificant variables to see how it would affect the model as a whole

and if it would change the significance of the other coefficients. This rigorous


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process yielded the following general model that best fits the data, based on aRandom Effects model:

Giniit= 1+ 2GDPpcit+ 3PrivSecit+ 4Inflait+ 5LIit+ 6MIit+ uit (1)

where,

Gini = Gini coefficientGDPpc = GDP per capitaPrivSec = private sector share of GDPInfla = inflation, GDP deflatorLI = Low Income dummy variable

MI = Middle Income dummy variablei = Countryt = Year

As shown, not every category is represented in this model.1 The results arepresented in Table 2.

Table 2. General (random effects) model results

Dependent variable: Inequality

Constant 20.69265**GDPpc 0.0003401*PrivSec 0.1561426**Infla 0.0030872**LI 11.21757**MI 6.33274*Source:authors estimation

Note: * p < 0.05, ** p < 0.01

5.2. Results

Due to the significance of the dummy variables in the REM, it can bediscerned that the income category to which a country belongs does have animpact on income inequality within a country.

On average, holding all independent variables at zero, the Ginicoefficient across time and between countries is 20.69265. It is particularlyrelated to the average level of income inequality within HI countries. On its own,

1This will be discussed further at the end of section 5.2.


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this is a fairly low level of inequality, as the Gini coefficient ranges from mostequal to least equal on a scale from 0 to 100 percent.

All else equal, an increase in GDP per capita by one unit across time andbetween countries results in a decrease in the Gini coefficient by 0.03401percent. This may seem like a small amount but one must note the size of theGDP per capita in this data set, which ranges from $321.0269725 for Moldova in1999 to $13,887.30239 for the Czech Republic in 2006.

The results from previous studies regarding the relationship betweenincome inequality and GDP per capita are ambiguous in terms of the effect thelatter has on the former, so there were no firm expectations before theregressions were run. This study finds that there is a statistically significantrelationship, bolstering the results found by Kuznets (1955) and Ogwang (1994).

The difference is that Kuznets and Ogwang looked at non-linear functions,resulting in an inverted-U shaped relationship, while this study foundsignificance in a linear relationship. Further investigation into potential non-linear relationships would be beneficial in the future study of GDP and incomeinequality, sharing the view of Aghion et al.(1999).

The next independent variable in the model is the private sector share ofGDP, measuring privatization as part of the political factors category. The

model indicates that increased privatization increases the level of incomeinequality. This can cause a significant impact on inequality as the private sector

share of GDP reached 70 to 80 percent during the 2000s for the majority of thecountries in the dataset. An increase in the private sector share of GDP wouldthus make quite a difference in the level of inequality in a country, especiallycompared to the very low levels of privatization seen during Communism and atthe start of the transition process.

The positive relationship found in this model reinforces the positiverelationship between privatization and income inequality found by Bandelj andMahutga (2010) as well as Grimalda et al. (2010). This positive relationship isfound using different variables to represent privatization; Bandelj and Mahutga(2010) considered the size of the private sector while Grimalda et al. (2010)

used the Private Share of Value Added. This strengthens the evidence of apositive relationship between privatization and income inequality as it is foundacross differing variables. A significant relationship between the two is of

particular importance as privatization was not a feature of the socialist system(Kaasa, 2005). Its inclusion in the analysis shows the effects of the transition

process and specifically the introduction of the market economy system in thesecountries.

The third independent variable is inflation, here measured as the growthrate of the GDP deflator. All else being held equal, an increase in inflation

increases the level of income inequality. As with the case of GDP per capita,increased inflation may appear to create a small change in income inequality but


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it makes a large difference in the Gini coefficient during years of hyperinflation,such as in Ukraine in 1993 when the growth rate of the GDP deflator reached an

annual percentage of 3334.798345 (World Bank, 2011f).Results from the work of Bulir (2001) and Parker (1996) provide theexpectation of a positive relationship between inflation and income inequality, asshown in this study. Bulirs model showed a non-linear relationship between thetwo, in that a reduction in a high rate of inflation had a larger impact on incomeinequality than the reduction in a low rate of inflation, while the model in thisstudy yields a linear relationship for this dataset. Herein lies another similaritybetween the interpretation of the GDP per capitas coefficient and inflations

coefficient as further study is recommended to analyze different functionalforms of relationships between inflation and income inequality. While some past

studies, for example Gustafsson and Johansson (1999), show an insignificantrelationship for both the fixed effects and the random effects estimates, thisstudy yields a statistically significant relationship in both models.

The final independent variables in this model are the dummy variables:LI and MI. For LI countries, the constant term becomes 31.91022. For MIcountries, the constant term is 27.02539. Thus, on average, LI and MI countrieshave higher levels of income inequality than HI countries, with LI countriesmore unequal than MI countries. Both of the dummy variables for LI and MI arestatistically significant at the 5 percent level, indicating that the income level of

a country, based on the categories created under the World Banks Atlas Method(World Bank, 2011i), does have an effect on the countrys level of incomeinequality as represented by the Gini coefficient. This finding agrees with thehypothesized result.

As the results have shown a relationship between the income categoryand income inequality, the next part of the hypothesis can be tested; differentincome categories yield different relationships between income inequality andthe factors that affect income inequality. This hypothesis was tested by runningthe same REM model for each income category and analyzing the coefficients inthe model. The models show differences in magnitude, the direction of the

relationships, and in their significance.

Table 3. Effect on low-income, middle-income, and high-income countries

LI MI HI

Constant 35.70937** 23.50054** 24.8364**GDPpc 0.008557** 0.001323 0.000065PrivSec 0.235328** 0.1868987** 0.0682318**Infla 0.0028549* 0.0081225** 0.0050133

Source:authors estimation

Note: * p < 0.05, ** p < 0.01


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It is important to take into account the fact that the number ofobservations is small, especially for the LI regression as only two countries are

included in this group. Thus, the results must be interpreted with caution. Furtherresearch would be necessary when data for a larger number of countries or alonger period of time can be included.

The LI model is overall statistically significant at a 5 percentsignificance level and each coefficient is significant at a 5 percent level as well.All of the signs of the coefficients are the same as in the original model. Thecoefficient for GDP per capita is larger, so it plays a bigger role for LI countriesthan for all transition countries studied as a group. The coefficient for the privatesector share of GDP is roughly 50 percent larger than the one found in theoriginal model, so privatization plays a bigger role than the average as well. The

coefficient for inflation is smaller than in the original model, but only slightly. Itis interesting to note that while the LI group only consists of two countries, it isthe closest to the original REM in sign and significance levels.

The MI model is also statistically significant at the 5 percent level, as isevery coefficient except for the GDP per capita. GDP per capita shows a positiverelationship, as opposed to a negative one, but it has an insignificant coefficient.The coefficient for the private sector is similar to the original one, while theeffect of inflation is shown to be greater.

Lastly, the HI model is also statistically significant at the 5 percent level,

however, neither GDP per capita nor inflation are significant. The effect of theprivate sector is less than half as large as in the original model, so privatizationdoes not have a strong relationship with income inequality in the HI countries.The results from the HI and MI models show that further research must be doneto determine which variables are of significance in terms of influencing incomeinequality for these two income categories. These differences between incomegroups show interesting policy implications, in that lower income countries musttake different factors into account when attempting to increase equality levelsrelative to higher income countries.

While the results are generally significant, there is the issue of

categories that are not represented in the model. There are no variablesaccounting for demographic factors or cultural and environmental factors inthe model of the best fit. It is particularly surprising that corruption was notsignificant, as it has such an impact on transition economies. The Control ofCorruption index could be inherently flawed as corruption is not easilymeasured. There could also be issues with the missing data points, as the numberof data points for this variable was limited.


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6. Conclusions

Income inequality in Central and Eastern European transition countrieshas increased after 1990 when the countries started their economic transitionfrom a centrally planned to a market economy, and political transition from anauthoritarian system to a democratic one. The level of income inequality differsacross country income categories, as do the relationships between thecontributing factors of income inequality and income inequality itself. Theimportance of this result is that it implies transition economies at different levelsof wealth should be analyzed separately when making policy decisions related toincome inequality, as the relationships between income inequality and thevariables in this study were shown to differ across income categories so policies

to decrease the disparity should differ across these categories as well. Policies todecrease inflation would benefit countries in the LI and MI categories, althoughthey would have to take into account relationships between inflation and othervariables such as, for example, unemployment. Policies related to privatizationwould impact inequality for all three income categories and governments wouldwant to search for policies that could support privatization yet mitigate the effectof high levels of privatization on income inequality.

Given the damaging effects of income inequality on a country, includingeconomic inefficiencies and socio-political instability as discussed in Section 2,

this study has aimed to provide insights into the factors that impact incomeinequality so as to better understand the relationships that matter in transitioneconomies. This study has found relationships using a broad range of factors, asopposed to a significant portion of the literature which focuses on only onefactor at a time. Future studies would benefit from utilizing differentrepresentative variables, different functional forms, and new data as it becomesavailable. Further research is needed in the field to improve the economic,

political, and social situations within transition economies through finding thebest policy responses that would result in decreased income inequality.

References

Aghion, P. and Commander, S.J. (1999), On the Dynamics of Inequality in theTransition,Economics of Transition, No. 7, pp. 275-298.

Aghion, P., Caroli, E. and Garcia-Penalosa, C. (1999), Inequality and Economic Growth:The Perspective of the New Growth Theories, Journal of EconomicLiterature,

No. 37, pp. 1615-1660.Alderson, A. and Nielsen, F. (1999), Income Inequality, Development, and Dependence:

A Reconsideration,American Sociological Review, No. 64, pp. 606-631.

Aristei, D. and Perugini, C. (2012), Inequality and Reforms in Transition Countries,Economic Systems, No. 36, pp. 2-10.


13/15


Bandelj, N. and Mahutga, M.C. (2010), How Socio-Economic Changes Shape IncomeInequality in Post-Socialist Europe, Social Forces, No. 88, pp. 2133-2161.

Beitz, C.R. (1982), Democracy in developing societies, in: Gastil, R. G. (ed.), Freedomin the World: Political Rights and Civil Liberties, New York: Greenwood Press.

Bruno, M., Ravallion, M. and Squire, L. (1998), Equity and Growth in DevelopingCountries: Old and New Perspectives on the Policy Issues, in: Tanzi, V. and Chu,K. (eds.),Income Distribution and High-QualityGrowth, Cambridge: MIT Press.

Bulir, A. (2001), Income Inequality: Does Inflation Matter?, IMF Staff Papers, No. 48,pp. 139-159.

Chowdhury, K. (2003), Empirics for World Income Distribution: What does the WorldBank Data Reveal?, The Journal of Developing Areas, No. 36, pp. 59-83.

Chu, H. (2000), The Impacts of Educational Expansion and Schooling Inequality onIncome Distribution, Quarterly Journal of Business and Economics, No. 39, pp.

39-49.Dao, M. (2008), Human Capital, Poverty, and Income Distribution in Developing

Countries,Journal of Economic Studies, No. 35, pp. 294-303.Deininger, K. and Squire, L. (1996), A New Data Set Measuring Income Inequality,

World Bank Economic Review, No. 10, pp. 565-591.Durham, J. (1999), Econometrics of Income Distribution: Toward more Comprehensive

Specification of Institutional Correlates, Comparative Economic Studies, No. 41,pp. 43-74.

European Bank for Reconstruction and Development (2011), Transition Report, London.European Bank for Reconstruction and Development (2013), Transition Report, London.Freedom House (2011),Freedom in the World 2011, Washington D.C.Gradstein, M. and Milanovic, B. (2004), Does Libert=Egalit? A Survey of the

Empirical Links between Democracy and Inequality with Some Evidence on theTransition Economies,Journal of EconomicSurveys, No. 18, pp. 515-537.

Grimalda, G., Barlow, D. and Meschi, E. (2010), Varieties of Capitalisms and Varietiesof Performances: Accounting for Inequality in Post-Soviet Union TransitionEconomies,International Review of AppliedEconomics, No. 24, pp. 379-403.

Grosfeld, I. and Senik, C. (2010), The Emerging Aversion to Inequality, Economics ofTransition, No. 18, pp. 1-26.

Gupta, S., Davoodi, H. and Alonso-Terme, R. (2002), Does Corruption Affect IncomeInequality and Poverty?,Economics of Governance, No. 3, pp. 23-45.

Gustafsson, B. and Johansson, M. (1999), In Search of Smoking Guns: What MakesIncome Inequality Vary over Time in Different Countries?, AmericanSociologicalReview, No. 63, pp. 585-605.

Ivanova, M. (2007), Inequality and Government Policies in Central and Eastern Europe,East European Quarterly, No. 41, pp. 167-204.

Izyumov, A. and Claxon, T. (2009), Models of Capitalism and Income Distribution inTransition Economies: A Comparative Perspective, Journal of Economic Issues,

No. 43, pp. 733-758.Kaasa, A. (2005), Factors of Income Inequality and their Influence Mechanisms: A

Theoretical Overview, University of Tartu - Faculty of Economics and Business

Administration Working Paper, No. 40.


14/15


Kaufmann, D. and Siegelbaum, P. (1997), Privatization and Corruption in TransitionEconomies,Journal of International Affairs, No. 50, pp. 419-458.

Kuznets, S. (1955), Economic Growth and Income Inequality, The American EconomicReview, No. 45, pp. 1-28.

Li, H., Squire, L., and Zou, H. (1998), Explaining International and IntertemporalVariations in Income Inequality,Economic Journal, No. 108, pp. 26-43.

Li, H., Xu, L., and Zou, H. (2000), Corruption, Income Distribution, and Growth,Economics and Politics, No.12, pp. 155-182.

Milanovic, B. (1998), Income, Inequality, and Poverty during the Transition fromPlanned to Market Economy, Washington: The World Bank Group.

Nielsen, F. and Alderson, A. (1995), Income Inequality, Development, and Dualism:Results from an Unbalanced Cross-National Panel, American SociologicalReview, No. 60, pp. 674-701.

Nielsen, F. and Alderson, A. (1997), The Kuznets Curve and the Great U-Turn: IncomeInequality in U.S. Counties, 1970 to 1990, American Sociological Review, No.62, pp. 12-33.

Nomiya, D. (1992), On the effects of urbanization on income inequality: A crossnational study, Shakaigaku Hyoron/Japanese Sociological Review, No. 43, pp.16-29.

Ogwang, T. (1994), Economic Development and Income Inequality: A NonparametricInvestigation of Kuznets' U-Curve Hypothesis, Journal of QuantitativeEconomics, No. 10, pp. 139-153.

Park, K.H. (1996), Income Inequality and Economic Progress: An Empirical Test of theInstitutionalist Approach, The American Journal of Economics and Sociology,

No. 55, pp. 87- 97.Parker, S. (1998), Income Inequality and the Business Cycle: A Survey of the Evidence

and some New Results, Journal of Post Keynesian Economics, No. 21, pp. 201-225.

Porras, L. (2010), Income Inequality in the Post-Socialist Transition: An InstitutionalApproach to the Czech, Hungarian and Russian Cases, Revue d'EtudesComparativesEst-Ouest, No. 41, pp. 69-109.

Psacharopoulos, G. and Steier, F. (1988), Education and the Labor Market in Venezuela,19751984,Economics of Education Review, No. 7, pp. 321-332.

Ravallion, M. (1997), Can High-Inequality Developing Countries Escape Absolute

Poverty?,Economics Letters, No. 56, pp. 51-57.Ravallion, M. (2001), Growth, Inequality and Poverty: Looking beyond Averages,

World Development, No. 29, pp. 1803-1815.Rudra, N. (2004), Openness, Welfare Spending, and Inequality in the Developing World,

International Studies Quarterly, No. 48, pp. 683-709.Samanta, S.K. and Heyse, A. (2006), Income Inequality and Economic Growth in

Developing Countries: An Empirical Analysis,Indian Journal of Economics andBusiness, No. 5, pp. 243-254.

Sylwester, K. (2002a), Can Education Expenditures Reduce Income Inequality,Economics of Education Review, No. 21, pp. 43-52.

Sylwester, K. (2002b), Democracy and Changes in Income Inequality, InternationalJournal of Business and Economics, No. 1, pp. 167-178.


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Todaro, M.P. and Smith, S.C. (2009), Economic Development, Boston: Pearson AddisonWesley.

Ullman, M. (1996), Urbanization and Income Inequality: Case Study within Brazil,American Sociological Association.

Wood, A. (1997), Openness and Wage Inequality in Developing Countries: The LatinAmerican Challenge to East Asian Conventional Wisdom, The World BankEconomic Review, No. 11, pp. 33-57.

World Bank (2011a), Country and Lending Groups, retrieved from:http://data.worldbank.org/about/countryclassifications/country-and-lending-groups.

World Bank (2011b), Foreign Direct Investment, Net Inflows (BoP, current US$),retrieved from: http://data.worldbank.org/ indicator/BX.KLT.DINV.CD.WD.

World Bank (2011c), GDP per Capita (current US$), retrieved from:

http://data.worldbank.org/ indicator/NY.GDP.PCAP.CD.World Bank (2011d), GNI per capita, Atlas Method (current US$), retrieved from:

http://data. worldbank.org/indicator/NY.GNP.PCAP.CD.World Bank (2011e), How We Classify Countries, retrieved from:

http://data.worldbank.org/ about/country-classifications.World Bank (2011f), Inflation, GDP Deflator (Annual %), retrieved from:

http://data.worldbank.org/ indicator/NY.GDP.DEFL.KD.ZG.World Bank (2011g), Primary Completion Rate, Total (% of relevant age group),

retrieved from: http://data.worldbank.org/indicator/SE.PRM.CMPT.ZS.World Bank (2011h), Rural Population (% of Total Population), retrieved from:

http://data.worldbank.org/indicator/SP.RUR.TOTL.ZS.World Bank (2011i), World Bank Atlas Method, retrieved from:

http://data.worldbank.org/about/ country-classifications/world-bank-atlas-methodWorld Bank (2011j), The Worldwide Governance Indicators (WGI) Project, retrieved

from:http://info.worldbank.org/governance/wgi/index.asp.
http://data.worldbank.org/indicator/NY.GNP.PCAP.CDhttp://data.worldbank.org/indicator/SP.RUR.TOTL.ZShttp://info.worldbank.org/governance/wgi/index.asphttp://info.worldbank.org/governance/wgi/index.asphttp://data.worldbank.org/indicator/SP.RUR.TOTL.ZShttp://data.worldbank.org/indicator/NY.GNP.PCAP.CD

income inequality in post-communist central and eastern european countries

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